TY  - EJOU
AU  - Mankodiya, Harsh 
AU  - Palkhiwala, Priyal 
AU  - Gupta, Rajesh 
AU  - Jadav, Nilesh Kumar 
AU  - Tanwar, Sudeep 
AU  - Alfarraj, Osama 
AU  - Tolba, Amr 
AU  - Raboaca, Maria Simona 
AU  - Marina, Verdes 

TI  - Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 77
IS  - 1
SN  - 1546-2226

AB  - The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online polling system. It provides a novel way to overcome security breaches during the face identity (ID) recognition and verification process for online polling systems. This verification is done by training a pixel-2-pixel <i>Pix2pix</i> generative adversarial network (GAN) for face image reconstruction to remove facial objects present (if any). Furthermore, image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome, thus checking the electorate credentials.
KW  - Artificial intelligence; discriminator; generator; <i>Pix2pix</i> GANs; Kullback-Leibler (KL)-divergence; online voting system; Siamese network

DO  - 10.32604/cmc.2023.038556